| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Aggregates 6 discrete OPVS skills (AgentBoard, AgentDocs, AgentMemory, OPVS Protocol, Auth, Integrations) into a single MCP server endpoint, eliminating per-MCP tool caps by bundling all capabilities under one Model Context Protocol connection. Uses a skill registry pattern to expose heterogeneous tool sets through a unified transport layer, allowing Claude Code and Cursor to access the full OPVS ecosystem without hitting individual tool limits.
Unique: Bundles 6 heterogeneous OPVS skills into a single MCP server to bypass per-MCP tool caps in Claude Code and Cursor, whereas competitors typically expose skills as separate MCP instances or require manual tool registration
vs alternatives: Eliminates tool cap friction for power users by consolidating all OPVS capabilities into one MCP connection, whereas managing 6 separate scoped packages requires multiple MCP registrations and context switching
Exposes AgentBoard's collaborative workspace and task management capabilities through MCP tool bindings, enabling AI agents to create, read, update, and delete board items, manage task hierarchies, and synchronize state across multiple agent instances. Implements a stateless tool interface that maps MCP calls to AgentBoard's REST API or internal data model, preserving board structure and permission semantics.
Unique: Exposes AgentBoard's task hierarchy and collaborative state as MCP tools, allowing agents to treat board operations as first-class capabilities rather than requiring custom API integration code
vs alternatives: Provides native MCP bindings to AgentBoard's workspace model, whereas generic REST API clients require agents to handle HTTP orchestration and state management manually
Integrates AgentDocs' documentation indexing and retrieval system as MCP tools, enabling agents to index code repositories, generate documentation, and query structured documentation artifacts. Uses a document embedding and retrieval pattern to make codebase knowledge queryable by agents without requiring them to parse source files directly.
Unique: Exposes AgentDocs' documentation generation and semantic search as MCP tools, allowing agents to treat documentation as a queryable knowledge base rather than static files
vs alternatives: Provides agent-native documentation indexing and retrieval, whereas RAG systems require agents to manage embeddings and vector stores separately
Exposes AgentMemory's persistent context storage and retrieval as MCP tools, enabling agents to save and recall conversation history, learned facts, and execution traces across sessions. Implements a key-value or document store interface where agents can write structured memory entries and query them with semantic or exact-match retrieval, preserving agent state without relying on prompt context alone.
Unique: Exposes AgentMemory as MCP tools for persistent agent state, allowing agents to maintain context across sessions without relying on prompt engineering or external state management
vs alternatives: Provides native MCP bindings for agent memory, whereas generic databases require agents to implement their own serialization and retrieval logic
Implements OPVS Protocol as MCP tools, enabling agents to communicate using OPVS' standardized message format and routing semantics. Provides tools for agent-to-agent messaging, capability discovery, and protocol-compliant request/response handling, allowing agents to interoperate within the OPVS ecosystem without custom protocol implementation.
Unique: Exposes OPVS Protocol as MCP tools, standardizing agent-to-agent communication and capability discovery without requiring agents to implement protocol logic
vs alternatives: Provides protocol-compliant agent communication through MCP, whereas custom agent frameworks require implementing routing and discovery from scratch
Exposes OPVS Auth system as MCP tools for credential management, token generation, and permission verification. Implements OAuth2/JWT patterns for secure agent authentication, allowing agents to obtain and refresh credentials, verify permissions, and access protected OPVS resources without embedding secrets in prompts or code.
Unique: Exposes OPVS Auth as MCP tools for credential management, allowing agents to authenticate and verify permissions without embedding secrets or implementing OAuth logic
vs alternatives: Provides centralized credential management through MCP, whereas custom auth integration requires agents to handle token lifecycle and permission checks manually
Exposes OPVS Integrations framework as MCP tools, enabling agents to connect to third-party services (APIs, databases, webhooks) through pre-configured integration adapters. Uses an adapter registry pattern where integrations are defined declaratively and exposed as MCP tools, allowing agents to invoke external services without implementing service-specific logic.
Unique: Exposes pre-configured third-party service integrations as MCP tools through an adapter registry, allowing agents to invoke external services without implementing service-specific logic
vs alternatives: Provides declarative service integration through MCP adapters, whereas generic HTTP clients require agents to implement authentication, error handling, and response parsing for each service
Automatically exposes all 6 OPVS skills' capabilities as MCP-compliant tool schemas with JSON Schema definitions, parameter validation, and return type specifications. Implements the Model Context Protocol's tool definition format, allowing MCP clients (Claude Code, Cursor) to discover available tools, validate inputs, and handle outputs without requiring manual schema registration.
Unique: Automatically generates and exposes MCP-compliant tool schemas for all 6 OPVS skills, enabling seamless tool discovery and validation in MCP clients without manual schema registration
vs alternatives: Provides automatic schema generation and exposure, whereas manual MCP integration requires hand-writing JSON Schema definitions for each tool
+1 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs @opvs-ai/mcp at 33/100. @opvs-ai/mcp leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities